NVIDIA AI Podcast

Generative AI can predict whether a plane takes off—but does it know why? Nicolas Cerisier, VP of 3DEXPERIENCE Platform R&D at Dassault Systèmes, explains how industrial world models go beyond pattern recognition to embed the actual laws of physics, chemistry, and engineering. In this episode of the NVIDIA AI Podcast, he also breaks down Dassault's three virtual companions (AURA, LEO, and MARIE), their 25-year collaboration with NVIDIA, and a stunning real-world use case: helping NIAR rebuild aircraft designs part by part, using AI.

What if one AI brain could run every robot on the planet—a humanoid, a warehouse arm, and a dog-like inspection bot—all at once?

That's not a thought experiment. That's what Skild AI is building right now.

Deepak Pathak (CEO and Co-Founder) and Abhinav Gupta (President and Co-Founder) of Skild AI join the pod to break down Skild Brain—a universal, general-purpose AI model designed to power robots of any form factor, tackling any task, from a single shared intelligence.

What happens when you combine AI with quantum computing? NVIDIA's Nic Harrigan joins the AI Podcast to break down the state of quantum, explain why error correction is the pivotal challenge, and reveal how NVIDIA Ising—the world's first open AI model family for quantum—is changing the game.

🔗 Resources mentioned:

► Read our NVIDIA Ising announcement 

► Learn more about NVIDIA Ising 

► Learn more about NVIDIA Quantum Computing

Chapters:

0:00 Intro

0:55 What is quantum computing?

4:00 Qubits, noise, and error correction

5:26 How AI helps quantum error correction

10:57 Applications: drug discovery and materials

15:33 NVIDIA Ising announcement

20:35 Scaling quantum hardware

27:31 Algorithm development with generative AI

Enterprises are moving from AI pilots to full‑scale AI factories that turn data into trusted digital intelligence. Red Hat CTO Chris Wright and NVIDIA’s Justin Boitano unpack the "five‑layer cake" AI factory stack, from accelerated hardware and hybrid cloud infrastructure to models, agents, and production‑grade governance.

AI is reshaping electricity demand. What does increased demand, and the shape of that demand, mean for the electric grid? Ben Sooter, Director of R&D at EPRI joins the podcast to explain why most of an AI model’s lifetime energy use comes from inference rather than training, and how micro data centers located near underutilized substations can help deliver low‑latency AI services while strengthening grid resilience.